Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences

arXiv:2305.11716v2 Announce Type: replace
Abstract: Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier correspondences, making outlier-robust registration techniques indispensable. Many recent studies have adopted the branch and bound (BnB) optimization framework to solve the correspondence-based point cloud registration problem globally and deterministically. Nonetheless, BnB-based methods are time-consuming to search the entire 6-dimensional parameter space, since their computational complexity is …

abstract arxiv cloud cs.cv current feature lidar making outlier paradigm registration replace residual robust scans search strategy studies through transformation type